Magnetic resonance imaging ("MRI") is a useful diagnostic procedure that analyzes changes in magnetic orientations to produce an image of an object under examination. Unfortunately, movement of the object during MRI relative to a reference position adversely affects the quality of the image produced.
FIG. 1 shows a typical MRI system 2, a system with which the present invention may be practiced. MRI system 2 includes a powerful typically superconducting magnet 4 that creates a longitudinal magnetic field h.sub.o along the z-axis when power supply 6 is energized. To promote homogeneity of field h.sub.o, magnet 4 includes shim coil mechanisms (not depicted) that are coupled via bus 8 to a computer system 10 that generates appropriate corrective shimming gradient signals.
The object 12 to be imaged is placed within a radio frequency ("RF") coil 14 within magnet 4, and is thus exposed to the magnetic field h.sub.o. Object 12 includes atoms whose nuclei have typically randomly oriented magnetic dipoles. During MRI, the h.sub.o field polarizes the population of such nuclei (or "spins"), causing the spins to align parallel to the magnetic field direction, here along the z-axis. A net magnetization M.sub.z results.
Computer 10 via bus 8 and RF coil 14 subjects the spins to a brief pulse of radio frequency ("RF") energy at a resonant frequency called the Larmor frequency (RF source not depicted), thus exciting the oriented spins. This excitation temporarily reorients the spin axis of the nuclei, tipping the nuclei magnetic moment towards a plane transverse to the z-axis, i.e., the x-y plane. After termination of the RF excitation pulse, the net transverse magnetization is detected (or acquired) in real time as RF signals through RF coil 14 and a receiver (not depicted).
These signals are digitized (digitizing mechanism not depicted) and coupled to computer 10 for processing. The acquired raw RF signals include magnetic field gradient information, and represent the spatial frequency information domain, or so-called raw k-space data matrix (or array). By using Fourier transformation upon the raw k-space data matrix, a reconstructed image 16 of object 12 may be obtained and displayed on monitor. However, if object 12 moved during scanning, the quality of image 16 can be degraded.
The amplitude of the detected nuclear magnetic resonance ("NMR") signal depends upon the degree of RF excitation and the relaxation properties of the spins. During imaging, computer system 10 controls magnetic field gradients G.sub.x, G.sub.y, G.sub.z along the x, y, z axes that are needed to spatially correlate locations of the spins in object 12.
Typically 256 y-axis gradient levels are used during an MRI scan, which levels (or phase encoding steps) range from -128 through 0 to +128, where the 0th phase encoding for a slice refers to the line (or "echo") having no phase encoding gradient. Each line or echo will comprise 256 points that correspond to one step along the y-axis in the so-called k-space (or momentum space) domain. Essentially these gradient levels allow system 2's sensitivity to be limited to a single x-y plane slice along the z-axis.
By suitably controlling the gradient strengths G.sub.x, G.sub.y, G.sub.z during each MRI image cycle, the spatial distribution of nuclei spin excitation may be controlled, and the location of the resulting NMR signals can be identified. The NMR data for reconstructing images may be collected using several known techniques, including multiple angle projection reconstruction and Fourier transform. Such techniques typically comprise a pulse sequence made up of a plurality of sequentially implemented views. Each view may include one or more NMR experiments, each using an RF excitation pulse and a magnetic field gradient pulse to encode spatial information into the resulting NMR signal. As is understood by those skilled in the relevant art, the NMR signal may be a free induction decay or, preferably, a spin-echo signal.
As further understood by those skilled in the relevant art, low frequency information including motion information is present only in the center of the raw k-space data, with higher frequency information appearing at the beginning and end of this data matrix.
Computer system 10 uses the gradient magnetic field information to identify where spatially the intensity values represented by the acquired k-space radio data belong. Such techniques are commonly found in clinical MRI systems using two dimensional multi-slice imaging sequences, wherein the acquisition of the data for the slices is interleaved in time.
Computer system 10 transforms the k-space data to a real space image using conventional spatial Fourier transform methods. The real space image 16 may then be displayed on a monitor 18, to show contrast changes as a function of x-axis and y-axis positions. Typically image 16 depicts a "slice" of object 12 in the x-y plane, at chosen different locations along the z-axis.
If object 14 moves during imaging (depicted by dashed lines 20), so-called motion artifacts result that can degrade the quality of the reconstructed image 16. However if object 12 motion could be measured, it would be possible to compensate for that movement and generate an improved reconstruction image.
Although the k-space data includes information that can identify object movement, it has been difficult in the prior art to measure such movement directly from the k-space data at all times during the scan. This difficulty arises because, as depicted in FIG. 2, the signal to noise ("S/N) ratio is poor during most of the image scan time.
FIG. 2 depicts a normal prior art collection of k-space data for a single slice along the z-axis. Assume that a total of 256 lines are to be scanned from time 0 to time 256, wherein one scan acquires data for one line. As noted, each line will have 256 data points that correspond to one k-space step along the y-axis, and collectively the 256 lines represent 256 magnetic field gradient levels along the y-axis. According to the prior art, the same acquisition order is used for each slice, e.g., here -128, through 0 to +127.
According to FIG. 2, although the scan starts at time t=0 (or line -128) when possibly object 10 movement occurs, the S/N is relatively low (e.g., poor) and artifact movement signals cannot be readily detected. It is not until about time t=96 to time t=159 that the S/N ratio improves adequately, with time t=128 (corresponding to line 0) representing peak S/N ratio. Thus t=128, which corresponds to a low order phase gradient (e.g., 0) representing low frequency data, when the center portion of the k-space data is acquired represents the best time to detect artifact signals representing motion, because at all other acquisition times the S/N is relatively inadequate.
Within this region of high S/N, object 12 movement could be detected, the movement displacement could be calculated, and a suitable correction could then be made computer system 10, using well known techniques, to improve the quality of the displayed reconstructed image 16. Unfortunately, however, object movement that occurred during time t=0 to time t=95, as well as movement occurring between time t=159 to t=256 will go unrecognized because the artifact signal is too submerged in the noise, due to poor S/N. Thus no displacement can be ascertained, no correction can be generated for such movement, and the resultant display image quality remains degraded. Stated differently, prior art approaches such as depicted in FIG. 2 are "blind" to object movement occurring other than at the period of optimum S/N.
One prior art attempt to reduce motion artifacts in a magnetic resonance imaging system is set forth in U.S. Pat. No. 4,937,526 issued to Ehman, et al. (1990). Applicants incorporate Ehman, et al. herein by reference, as this patent provides a very detailed description of MRI theory, accompanied by numerous explanatory figures.
Ehman, et al. discloses a method wherein additional RF and magnet gradient pulses are generated to create a second data matrix (in k-space) having a maximum S/N at all lines. This second matrix is then used to provide positional information identifying artifact movement. The positional change information is then used to improve the resultant image quality.
While Ehman, et al.'s system improves image quality, the improvement requires considerable overhead. For example, Ehman, et al.'s technique requires additional imaging time due to the necessary generation of additional RF and gradient pulses. Further, Ehman, et al. generates essentially twice the amount of initial k-space data to be stored in memory 14, and presents additional data to be processed by computer system 10, which processing requires additional time.
What is needed for MRI is a method that can recognize and compensate for object 12 motion, utilizing the k-space data itself generated during normal MRI scanning. Preferably such method should not require that memory 14 be increased beyond what is needed to store the image k-space data itself, should not require the generation of additional RF and gradient pulses (thereby not extending imaging time), and should not impose substantial additional processing time upon computer system 10. Such a method preferably should measure object translational movement in the readout direction by self-calibration, using only high S/N echoes from different slices. The present invention discloses such a method.